0.1 Installation and import

Questools can be installed through github:

# devtools::install_github("bAIo-lab/Questools")
library(Questools)

0.2 Dateset

We’ll use a health Questionnaire dataset to work with Questools. The dataset contains 291 variables, including 36 continuous (i.e., laboratory measurements), and 255 categorical variables (i.e., questions) having no missing data.

data <- read.csv("../data/data.csv")
kableExtra::kable(head(data)) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "200px")
Age Vazn Ghad BMI MetaP CharbiE FesharS FesharD Nabz DarsadChB DarsadAzB DorBadan DorBasan DorGardan FSG Urea UAC Chol TG Crea LDH CBC WBC RBC HB Hct MCV MCH MCHC Platelet Lymph Mxd Neut RDW PDW MPV test1 test2 test3 test4 test5 test6 test7 test8 test9 test10 test11 test12 test13 test14 test15 test16 test17 test18 test19 test20 test21 test22 test23 test24 test25 test26 test27 test28 test29 test30 test31 test32 test33 test34 test35 test36 test37 test38 test39 test40 test41 test42 test43 test44 test45 test46 test47 test48 test49 test50 test51 test52 test53 test54 test55 test56 test57 test58 test59 test60 test61 test62 test63 test64 test65 test66 test67 test68 test69 test70 test71 test72 test73 test74 test75 test76 test77 test78 test79 test80 test81 test82 test83 test84 test85 test86 test87 test88 test89 test90 test91 test92 test93 test94 test95 test96 test97 test98 test99 test100 test101 test102 test103 test104 test105 test106 test107 test108 test109 test110 test111 test112 test113 test114 test115 test116 test117 test118 test119 test120 test121 test122 test123 test124 test125 test126 test127 test128 test129 test130 test131 test132 test133 test134 test135 test136 test137 test138 test139 test140 test141 test142 test143 test144 test145 test146 test147 test148 test149 test150 test151 test152 test153 test154 test155 test156 test157 test158 test159 test160 test161 test162 test163 test164 test165 test166 test167 test168 test169 test170 test171 test172 test173 test174 test175 test176 test177 test178 test179 test180 test181 test182 test183 test184 test185 test186 test187 test188 test189 test190 test191 test192 test193 test194 test195 test196 test197 test198 test199 test200 test201 test202 test203 test204 test205 test206 test207 test208 test209 test210 test211 test212 test213 test214 test215 test216 test217 test218
55 114.7 163 43.0 1868 15 137 76 62 55.0 20.0 121 149 37 129.0000 38.00000 4.100000 250.0000 131.0000 0.700000 75.00000 5416.166 6300.000 4.85 13.80 41.3 85.2 28.5 33.4 268000.0 38.1 10.1 51.8 13.5 11.1 8.8 4 2 5 1 1 1 1 1 3 2 5 1 5 1 4 4 5 2 1 2 3 5 4 4 4 3 2 5 5 5 1 1 4 4 3 1 4 2 4 2 2 2 2 2 3 1 2 3 2 3 1 1 1 3 4 4 4 4 3 3 4 1 2 4 1 1 1 1 1 2 1 4 1 1 2 2 2 1 2 1 4 3 1 4 1 2 5 2 1 4 1 1 2 2 2 2 2 2 5 2 2 2 1 5 2 2 1 4 1 1 2 2 2 2 2 5 2 5 2 1 1 4 5 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 5 4 3 5 1 2 1 5 3 5 1 5 5 1 1 1 4 1 1 1 5 1 1 1 2 2 5 5 5 4 5 2 2 3 4 2 1 2 5 4 1 1
57 90.8 168 24.2 1842 18 138 88 72 32.9 30.2 47 117 42 102.0000 34.00000 7.300000 306.0000 123.0000 1.000000 46.00000 241.100 6166.572 6200.00 5.37 14.0 42.5 79.1 26.1 32.9 156000.0 38.4 12.4 49.2 13.9 12.3 4 1 5 1 1 2 1 1 3 3 2 1 5 1 4 2 3 2 2 3 1 1 1 2 1 1 1 5 4 2 1 2 1 3 1 1 2 2 1 2 3 1 1 1 2 1 2 3 3 1 1 1 1 3 1 2 1 3 1 2 1 2 3 5 2 1 1 1 1 1 2 5 2 2 2 2 2 1 2 2 5 3 1 4 1 2 2 1 2 5 2 2 2 2 2 2 2 2 5 2 2 2 1 5 2 2 2 5 5 2 2 2 2 2 2 5 2 5 2 2 2 4 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 1 1 1 1 3 2 1 2 1 1 1 5 5 1 1 1 4 1 1 1 5 1 1 1 5 1 5 4 4 5 5 2 3 3 2 3 1 1 1 3 5 2
43 90.7 178 28.6 1873 12 130 80 85 26.2 34.7 99 112 44 103.0000 19.00000 5.300000 221.0000 156.0000 0.800000 44.00000 5416.166 8400.000 5.37 16.90 47.4 88.3 31.8 36.1 257000.0 39.0 12.7 48.3 13.3 11.6 9.5 3 1 2 1 1 2 4 1 3 3 3 5 5 3 2 3 3 3 2 1 4 1 4 4 4 4 2 2 2 4 1 5 2 1 5 2 2 2 4 1 3 4 4 4 3 4 3 4 4 4 4 4 4 3 4 4 3 4 4 5 4 2 3 5 2 1 1 1 1 2 2 5 2 2 2 2 2 1 1 2 5 3 1 4 1 2 2 2 2 5 2 2 2 2 2 2 2 2 5 2 2 2 1 5 2 2 2 5 5 2 2 2 2 2 2 5 2 5 2 2 1 3 5 5 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 4 2 2 2 1 1 5 2 2 1 5 1 5 2 5 5 1 1 1 4 1 2 3 5 3 5 1 5 3 2 3 3 4 5 4 4 3 4 2 4 1 2 3 5 2
40 59.8 151 26.0 1235 7 140 80 70 39.7 25.4 93 102 33 98.0000 31.00000 4.200000 162.0000 156.0000 0.900000 38.00000 5416.166 7800.000 4.43 14.70 40.8 92.1 33.2 36.0 252000.0 38.8 15.5 45.7 12.4 11.0 9.1 3 2 4 1 1 3 3 1 4 3 3 1 5 1 4 5 5 2 1 2 1 1 1 1 4 1 2 5 4 2 4 1 3 4 2 2 2 2 2 3 1 1 2 2 1 1 2 2 2 2 1 1 1 2 1 1 2 3 1 3 2 2 3 5 2 1 1 1 1 2 2 5 2 2 2 2 2 2 2 2 5 3 1 4 1 2 2 2 2 5 2 2 2 2 2 2 2 2 5 2 2 1 1 2 2 1 2 5 5 2 2 2 2 2 2 5 2 5 2 2 2 4 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 1 2 2 3 2 1 2 3 5 1 5 5 1 1 1 4 1 4 3 5 2 4 1 5 3 3 2 4 2 5 2 2 2 3 2 1 1 5 4 5 2
23 52.0 160 20.0 1217 3 100 70 89 30.0 26.5 84 95 32 90.0000 34.00000 2.200000 185.0000 87.0000 0.700000 63.00000 108.600 6166.572 6400.00 4.51 13.1 39.1 86.7 29.0 33.5 306000.0 32.5 8.6 58.9 11.5 10.3 1 2 2 1 1 5 5 2 2 2 2 2 1 4 4 5 5 3 2 2 4 1 1 2 2 2 1 1 1 3 2 1 4 2 2 1 1 2 1 4 1 2 2 1 2 4 1 2 1 4 1 3 3 2 2 1 3 2 1 3 2 2 3 5 2 1 1 1 1 2 2 5 2 2 2 2 2 2 2 2 5 3 1 4 1 2 2 2 2 5 2 2 2 2 2 2 2 2 5 2 2 2 1 5 2 2 2 5 5 2 2 2 2 2 2 5 2 5 2 2 2 4 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 4 1 2 2 2 2 1 1 4 2 1 5 5 1 1 1 4 1 5 1 4 3 5 5 5 5 2 3 3 3 5 3 4 3 4 2 1 2 3 4 5 2
28 50.7 161 23.0 1285 4 130 80 75 34.4 27.4 76 98 34 106.4269 31.08092 4.495666 194.7283 162.1011 0.930414 48.76663 73.000 6166.572 5600.00 4.66 13.9 41.0 88.0 29.8 33.9 205000.0 42.4 9.4 48.2 12.0 14.1 1 2 5 1 1 4 2 4 3 3 2 2 1 4 4 5 5 3 2 2 4 1 1 2 2 2 1 1 2 3 2 1 3 2 2 1 1 2 1 3 1 2 2 1 2 3 1 2 1 3 1 1 1 2 2 1 3 2 1 3 2 2 3 5 2 1 1 1 1 2 2 5 2 2 2 2 2 2 2 2 5 3 1 4 1 2 2 2 2 5 2 2 2 2 2 2 2 2 5 2 2 2 1 5 2 2 2 5 5 2 2 2 2 2 2 5 2 5 2 2 2 4 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 4 1 2 2 2 2 1 1 4 2 1 5 5 1 1 1 4 1 5 1 4 3 5 5 5 5 2 3 3 3 5 3 4 3 4 2 1 2 3 4 5 2

0.3 dim.reduce

Use dim.reduce to visualize the continuous variables in 2 dimensions with tsne.

#dim.reduce(data[,1:36], method = "tsne") -> plt
#plt

0.4 ggm

Use ggm to construct a Gaussian Graphical Model with glasso and significance test method.

ggm(data[,1:36], significance = 0.05, rho = 0.1, community = TRUE, methods = c("glasso", "sin")) -> g
g$network
g
## $graph
## IGRAPH e5fcce4 UN-- 36 76 -- 
## + attr: label_1 (v/c), label_2 (v/c), name (v/c), weight_1 (e/n),
## | weight_2 (e/n)
## + edges from e5fcce4 (vertex names):
##  [1] PDW     --MPV      RDW     --MPV      Mxd     --Neut    
##  [4] Lymph   --RDW      Platelet--MPV      Platelet--Mxd     
##  [7] MCHC    --Mxd      MCH     --RDW      MCH     --Neut    
## [10] MCH     --Mxd      MCH     --Lymph    MCH     --Platelet
## [13] MCV     --Mxd      MCV     --Lymph    MCV     --MCHC    
## [16] Hct     --Mxd      Hct     --Platelet Hct     --MCHC    
## [19] HB      --RDW      HB      --MCHC     HB      --MCV     
## + ... omitted several edges
## 
## $betweenness
##         Age        Vazn        Ghad         BMI       MetaP     CharbiE 
## 267.9166667   9.9880952  94.7738095   6.6190476   3.5833333 108.8928571 
##     FesharS     FesharD        Nabz   DarsadChB   DarsadAzB    DorBadan 
##  33.0000000   0.0000000   0.0000000   6.7857143   0.3333333   6.0357143 
##    DorBasan   DorGardan         FSG        Urea         UAC        Chol 
##   0.8333333   8.2380952 273.0000000   0.0000000 143.5000000   0.0000000 
##          TG        Crea         LDH         CBC         WBC         RBC 
## 282.5000000  33.0000000 128.5000000   1.3095238  14.1666667  15.0000000 
##          HB         Hct         MCV         MCH        MCHC    Platelet 
##  16.5190476   6.9166667   5.4952381  38.3523810   6.4023810 124.7595238 
##       Lymph         Mxd        Neut         RDW         PDW         MPV 
##   6.1666667  22.4523810   1.1166667 121.8428571 283.5000000 273.5000000 
## 
## $network

0.5 bn

Use bn to learn the structure of a Bayesian network fitting data with si.hito.pc as a Constraint-based algorithm, mmhc as a Hybrid algorithm, and tabu as a Score-based algorithm. Repeat the bootstrap sampling 100 times. (very time consuming)

bn(data = data[,1:36], C.alg = c("si.hiton.pc"), S.alg = c("mmhc", "tabu"), blacklist = data.frame(to = "BMI", to = "Age"), R = 10, community = FALSE, str.thresh = 0.9, dir.thresh = 0.5) -> n
n$network
n
## $graph
##           from        to strength direction
## 5          Age   CharbiE      1.0 0.9000000
## 6          Age   FesharS      1.0 0.8000000
## 14         Age       FSG      1.0 0.3000000
## 38        Vazn       BMI      1.0 0.5500000
## 39        Vazn     MetaP      1.0 0.8500000
## 40        Vazn   CharbiE      1.0 0.9000000
## 46        Vazn  DorBadan      1.0 1.0000000
## 47        Vazn  DorBasan      1.0 1.0000000
## 79        Ghad DarsadChB      1.0 0.2000000
## 83        Ghad DorGardan      0.9 1.0000000
## 110        BMI   CharbiE      1.0 0.4000000
## 114        BMI DarsadChB      1.0 1.0000000
## 180    CharbiE     MetaP      1.0 0.8000000
## 181    CharbiE   FesharS      0.9 1.0000000
## 186    CharbiE  DorBadan      1.0 0.8000000
## 217    FesharS   FesharD      1.0 0.9000000
## 318  DarsadChB      Ghad      1.0 0.8000000
## 327  DarsadChB  DorBasan      1.0 1.0000000
## 355  DarsadAzB     MetaP      1.0 0.8000000
## 360  DarsadAzB DarsadChB      1.0 0.7000000
## 397   DorBadan  DorBasan      1.0 1.0000000
## 398   DorBadan DorGardan      1.0 1.0000000
## 462  DorGardan   FesharS      0.9 0.7777778
## 468  DorGardan  DorBasan      1.0 1.0000000
## 497        FSG   FesharS      1.0 0.9000000
## 526       Urea       Age      1.0 0.8000000
## 540       Urea       FSG      0.9 0.5555556
## 575        UAC       FSG      1.0 0.4500000
## 579        UAC      Crea      1.0 0.6500000
## 585        UAC       Hct      0.9 0.7777778
## 594        UAC       PDW      0.9 0.3888889
## 596       Chol       Age      1.0 1.0000000
## 613       Chol        TG      1.0 0.5500000
## 615       Chol       LDH      1.0 0.8000000
## 645         TG       FSG      1.0 0.7500000
## 647         TG       UAC      1.0 0.8500000
## 648         TG      Chol      1.0 0.4500000
## 650         TG       LDH      1.0 0.7000000
## 652         TG       WBC      0.9 1.0000000
## 675       Crea DarsadChB      1.0 0.7000000
## 681       Crea      Urea      1.0 0.8000000
## 690       Crea       Hct      0.8 0.8125000
## 699       Crea       PDW      0.8 0.3750000
## 827        RBC       CBC      1.0 0.6000000
## 832        RBC       MCH      1.0 0.3500000
## 835        RBC     Lymph      1.0 0.7500000
## 838        RBC       RDW      1.0 1.0000000
## 863         HB       WBC      1.0 1.0000000
## 865         HB       Hct      1.0 0.9500000
## 866         HB       MCV      1.0 0.2000000
## 941        MCV       Mxd      1.0 0.5500000
## 977        MCH      Neut      1.0 0.3000000
## 1117       Mxd      Neut      1.0 0.6000000
## 1143      Neut       WBC      1.0 0.8000000
## 1211       PDW       LDH      0.9 0.7222222
## 1225       PDW       MPV      1.0 0.8000000
## 1259       MPV       RDW      1.0 0.6000000
## 
## $network

0.6 min.forst

Employ min.forest with BIC to construct a mixed-interaction model fitting the data.

min.forest(data, stat = "BIC", community = TRUE) -> mf
mf$network
mf
## $summary
## gRapHD object
## Number of edges       = 1001
## Number of vertices    = 254
## Model                 = continuous 
## Statistic (minForest) = BIC
## Statistic (stepw)     = BIC
## Statistic (user def.) = 
## Edges (minForest)     = 1...253
## Edges (stepw)         = 254...1001
## Edges (user def.)     = 1...253
## 
## 
## $betweenness
##          Age        test1          Hct           HB          RBC 
## 5.020000e+02 1.139947e+04 1.549206e+00 2.680199e+02 2.589709e+02 
##        Lymph          MCH          RDW          Mxd          MCV 
## 0.000000e+00 1.520735e+02 1.714009e+02 2.930952e+00 4.196755e+02 
##         MCHC      test156      test155         Neut          CBC 
## 2.154365e+00 1.255000e+02 1.255000e+02 0.000000e+00 1.306940e+02 
##      test168      test169       test55       test56      test148 
## 1.006624e+01 2.107556e+02 1.160397e+04 0.000000e+00 1.678333e+02 
##      test147     Platelet      test115      test118        test9 
## 0.000000e+00 0.000000e+00 1.369430e+03 0.000000e+00 8.471903e+01 
##      FesharS      test107      test109          WBC     DorBadan 
## 1.922778e+02 2.469405e+02 4.354875e+01 1.428571e-01 7.078053e+02 
##     DorBasan      test121      test123          BMI       test11 
## 2.324816e+02 5.501543e+02 4.008822e+01 2.901087e+02 0.000000e+00 
##       test89       test90       test67       test66      CharbiE 
## 1.916419e+03 0.000000e+00 2.115667e+02 3.650000e+00 7.975781e+02 
##          PDW          MPV      test116         Vazn    DarsadChB 
## 3.105380e+02 1.404977e+02 3.333333e-01 1.338376e+03 1.048889e+03 
##       test10      FesharD    DarsadAzB       test23       test21 
## 3.333333e-01 0.000000e+00 3.068576e+03 5.781621e+02 0.000000e+00 
##       test93       test96       test62       test64        test2 
## 2.333333e+00 0.000000e+00 2.390799e+03 5.372167e+02 4.499763e+03 
##      test102      test104         Crea          UAC      test131 
## 7.623790e+02 0.000000e+00 1.489056e+02 5.864777e+02 3.333333e-01 
##      test130      test184      test183       test80       test81 
## 0.000000e+00 6.665271e+02 1.229430e+01 2.211805e+03 2.250000e+00 
##       test71       test72      test182       test45       test43 
## 7.059152e+02 0.000000e+00 1.883781e+02 4.001539e+02 4.107369e+02 
##       test61       test59       test94        MetaP      test154 
## 5.506635e+02 2.937909e+03 6.132332e+02 3.633254e+00 0.000000e+00 
##       test60       test47       test52       test57      test108 
## 5.934584e+03 8.536685e+01 1.233075e+03 1.205387e+03 0.000000e+00 
##       test54    DorGardan        test5        test4       test58 
## 2.530774e+03 1.870166e+03 0.000000e+00 1.217121e+01 1.236246e+03 
##      test138      test139      test167      test166         Ghad 
## 6.733333e+00 1.776500e+02 4.189404e+01 0.000000e+00 5.141695e+00 
##        test6       test98      test100       test75       test76 
## 5.445673e+02 1.960000e+03 0.000000e+00 2.520000e+02 0.000000e+00 
##       test44       test53      test124       test49       test41 
## 5.423093e+01 1.300473e+02 0.000000e+00 0.000000e+00 1.804463e+03 
##      test161      test160      test152      test150       test33 
## 0.000000e+00 0.000000e+00 1.246000e+03 0.000000e+00 3.050347e+01 
##       test40      test203      test204      test111      test113 
## 3.256479e+01 5.538252e+02 7.686407e+01 4.932429e+03 0.000000e+00 
##      test105         Urea      test122       test95      test172 
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 8.094780e+02 
##       test46      test202       test35      test171      test170 
## 0.000000e+00 2.903914e+02 1.047044e+03 9.824804e+00 1.886648e+01 
##      test132      test158      test159       test38       test74 
## 2.523333e+02 1.606107e+01 0.000000e+00 5.227475e+02 0.000000e+00 
##      test205       test39       test99       test36      test103 
## 7.537114e+01 9.666856e+01 1.482000e+03 2.006977e+03 0.000000e+00 
##       test51       test50       test65       test48       test34 
## 3.615653e+03 3.358870e+01 9.066667e+00 5.059600e+02 0.000000e+00 
##      test146       test42       test25      test142      test143 
## 3.351667e+02 1.926726e+01 2.746363e+00 2.932883e+03 1.575483e+03 
##      test185      test162      test163      test198       test85 
## 2.520000e+02 5.020000e+02 5.981757e+02 6.106198e+02 3.078938e+01 
##      test164      test165       test37      test112       test19 
## 2.520000e+02 0.000000e+00 0.000000e+00 4.872000e+03 6.129036e+01 
##      test127      test128      test178      test177       test83 
## 1.691167e+02 0.000000e+00 3.225541e+01 3.731129e+00 2.483333e+00 
##      test201       test18      test125           TG         Chol 
## 0.000000e+00 1.820096e+02 5.791127e+01 0.000000e+00 0.000000e+00 
##      test151          FSG      test206       test69      test134 
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.813500e+02 
##      test135       test24       test12       test13       test92 
## 1.065900e+03 2.049665e+02 1.153059e+02 0.000000e+00 1.441131e+03 
##       test88       test26      test186      test117      test119 
## 0.000000e+00 0.000000e+00 1.798380e+02 0.000000e+00 1.593870e+02 
##       test78       test14       test16       test22      test110 
## 0.000000e+00 2.610661e+02 1.048874e+01 4.343678e+02 0.000000e+00 
##       test15       test17        test3      test174      test120 
## 2.046063e+02 0.000000e+00 2.978758e+02 2.236597e+03 0.000000e+00 
##       test68       test84      test215      test173      test216 
## 2.500000e-01 2.066667e+00 5.000000e-01 5.611173e+01 1.357381e+02 
##       test82      test218      test213      test194      test190 
## 0.000000e+00 2.879051e+02 5.902931e+01 4.651234e+02 2.162900e+02 
##      test136      test211          LDH       test87      test106 
## 6.250000e+00 4.520202e-01 2.151969e+02 0.000000e+00 2.000000e+00 
##      test175      test176      test180       test30       test29 
## 2.520000e+02 0.000000e+00 1.324505e+02 5.062534e+00 3.611111e-01 
##      test144      test199      test140      test126      test197 
## 0.000000e+00 6.752106e+01 8.008333e+01 8.283333e+01 6.592891e+02 
##       test70       test91      test208       test73      test200 
## 6.976161e+01 2.426914e+01 1.173391e+02 0.000000e+00 5.380347e+02 
##       test63       test28      test193      test192      test179 
## 3.333333e-01 4.832816e+02 3.333333e-01 1.166667e+00 0.000000e+00 
##      test101      test209      test188      test189      test191 
## 1.960919e+02 0.000000e+00 1.158810e+03 6.541991e+01 0.000000e+00 
##       test79       test20      test207       test97       test31 
## 2.120539e+02 0.000000e+00 0.000000e+00 1.371001e+02 0.000000e+00 
##       test32      test114      test196      test195      test212 
## 0.000000e+00 3.778014e+02 1.607576e+00 0.000000e+00 3.940397e+00 
##      test210       test86      test217      test145       test77 
## 0.000000e+00 1.416667e+00 0.000000e+00 7.863333e+02 5.190811e+01 
##        test8      test214      test137      test153        test7 
## 0.000000e+00 0.000000e+00 2.066667e+02 0.000000e+00 0.000000e+00 
##      test141      test187      test133       test27      test181 
## 1.482000e+03 5.694024e+02 0.000000e+00 0.000000e+00 0.000000e+00 
##         Nabz      test149      test157      test129 
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 
## 
## $network

0.7 div

Use div with 1000 times of permutation to find the most deferentially answered questions between people with answer “1” to the test 62 and people with answer “2” to the test 62. (Test 62 is about cardiovascular diseases.)

g1 <- which(data$test62 == "1") 
g2 <- which(data$test62 == "2")
div(data[37:dim(data)[2]], g1, g2, permute = 1000) -> KL
KL$name <- row.names(KL)
KL <- KL %>% arrange(desc(KL))
kableExtra::kable(head(KL,n = 20)) %>%
  kable_styling() %>%
  scroll_box(width = "400px", height = "400px")
KL p.value name
15.7933761 0.01 test62
4.6857327 0.01 test65
4.2716551 0.01 test63
3.6990622 0.01 test64
2.2007322 0.01 test67
1.8612079 0.01 test66
0.9330294 0.01 test168
0.9090498 0.01 test1
0.8622886 0.01 test169
0.6740090 0.01 test69
0.5740225 0.01 test71
0.5268477 0.01 test174
0.4537840 0.01 test68
0.4197713 0.01 test54
0.4043018 0.01 test57
0.3865165 0.01 test178
0.3788912 0.01 test184
0.3759118 0.01 test73
0.3699828 0.01 test89
0.3543039 0.01 test80

0.8 div2

Use div2 to find the most deferentially answered questions between people with relatively high BMI (relative to blood pressure) and people with high pressure (relative to BMI). (FesharS demonstrates blood pressure.)

div2(data[37:dim(data)[2]], var1 = data$BMI, var2 = data$FesharS, permute = 0) -> KL
KL$name <- row.names(KL)
KL <- KL %>% arrange(desc(KL))
kableExtra::kable(head(KL, n = 20)) %>%
  kable_styling() %>%
  scroll_box(width = "300px", height = "400px")
KL name
5.8395634 test11
0.7040959 test1
0.5497792 test2
0.4742267 test8
0.4487907 test172
0.3703050 test89
0.3502208 test174
0.3422061 test121
0.3240845 test78
0.3101447 test56
0.3010604 test55
0.2995396 test71
0.2895905 test178
0.2648403 test57
0.2594900 test124
0.2542359 test6
0.2417774 test74
0.2400237 test80
0.2140980 test91
0.2092262 test195

0.9 plot

Use plot for different visualizations. levels is set to 10 to capture categorical variables.

Histogram for test 10:

plot(data, vars = c("test10"), levels = 10)

Density plot for BMI:pl

plot(data, vars = c("BMI"), levels = 10)

Boxplot of BMI for different answers of test 1:

plot(data, vars = c("test1", "BMI"), levels = 10)

Relative histogram of test 1 vs test 10:

plot(data, vars = c("test1", "test10"), levels = 10)

Scatter plot of Vazn (weight) vs BMI:

plot(data, vars = c("Vazn", "BMI"), levels = 10)

Pie chart of test 1:

plot(data, vars = c("test1"), levels = 10, pie = TRUE)